Health indicators (HIs) are greatly significant in sustaining the stable operation of machines and guiding the implementation of predictable maintenance. Traditional HIs such as kurtosis, negentropy, Gini index manifest some concessions in certain scenarios. In this study, we propose a new geometric partition L-Kurtosis (GLK) indicator for bearing condition monitoring leveraging its splendid fault characterization, and it is proved to be advantageous in perceiving bearing incipient fault occurrence and outlining sufficiently bearing full-life degradation trend through substantial experiments with run-to-failure datasets. Integrated with empirical wavelet transform, a GLKgram is constructed to enable the application of the GLK indicator in bearing incipient fault detection. The experimental results deliver that the GLKgram excels at recognizing the fault repetitive transients and resisting the interfering large random shocks and strong noise, further manifesting the superiority of the GLK indicator in identifying fault features.
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